Borrowing it
Nothing to install: this file belongs to banshee-data/velocity.report. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/banshee-data/velocity.report/main/.claude/skills/run-experiments/SKILL.mdgit clone --depth 1 https://github.com/banshee-data/velocity.reportWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/banshee-data/velocity.report/run-experiments)<a href="https://agentmods.dev/skills/banshee-data/velocity.report/run-experiments"><img src="https://agentmods.dev/badge/skills/banshee-data/velocity.report/run-experiments/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/banshee-data/velocity.report/run-experiments"><img src="https://agentmods.dev/badge/skills/banshee-data/velocity.report/run-experiments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.03615 |
| Opus 5.5 | $0.00036 | $0.01446 |
| Sonnet 5.5 | $0.00018 | $0.00723 |
| Haiku 4.5 | $0.00009 | $0.00362 |
Grade C, and why
run-experiments scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Never `--no-verify`, `rm -rf`, `git reset --hard` or a bare `git stash`. Commit with explicit Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
(`curl -sI https://huggingface.co | head -1`); if it does not, run kirk0 only and say so. A whole How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: run-experiments
Run experiments unattended until the time is up: choose the questions that move the product most, answer each under a rule written before its replays, make what passes the default, and leave a report, a PR and a summary for each. The method is the one the box rotation and box alignment runs used (geom0005 to geom0010, l5id0001 to l5id0005).
Usage
/run-experiments # 12 hours
/run-experiments 5h # a set duration: h and m, e.g. 90m or 2h30m
/run-experiments 6h l5id # only this area's questions, or named slugs (l5id0006 geom0011)
1. The clock
Record the start (date -u) and the deadline. Stop starting new arms once the time left is less
than the next arm's estimate plus the time to write it up: keep the last hour, or a sixth of a run
shorter than six hours, for reports, PRs and the summary. Check the clock between steps, not only
at the end. Never wait with a foreground sleep: run long jobs in the background and wait with an
until-loop or the Monitor tool, and keep checking disk and the clock while they run.
2. Where the run is
Find the host before choosing anything: it decides the captures, the scratch space and where raw output may go.
hostname; uname -sm; echo "${CLAUDE_CODE_REMOTE:-}"; df -h . /tmp
ls /Volumes/lidar/lidar/pcaps /Volumes/banshee-captures/lidar /mnt/captures/lidar 2>/dev/null | head
Local (the Mac, or any machine with captures attached).
- Captures: any PCAP present.
LIDAR_PCAP_DIRfromlocal.mk(on the Mac/Volumes/lidar/lidar/pcaps: S2 segments unders2/, whole placements undersf-street-speeds/raw/lidar/), the NAS at/Volumes/banshee-captures/lidar, and kirk0. - Scratch:
~/vr-scratch/<run>/on the internal disk. - Raw output: archived to
/Volumes/banshee-results/velocity-campaign/<run>/, the results share onarrow.local. LiDAR data goes on no other volume.
arrow-worker (the VM bansheeworker on the TrueNAS host arrow).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +25 lines f2593b439e58
- 14d ago Changed · +2 lines a4cec98727c7
- 19d ago First seen · 189 lines · 90 tokens per session scan C 3938c89472b6
run-experiments is a skill published in the GitHub repository banshee-data/velocity.report (11 stars, last pushed today), licensed Apache-2.0. It adds 90 tokens to every session and 3,615 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-18.
run-experiments compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| run-experimentsbanshee-data/velocity.report | Skill | 11 | today | Apache-2.0 | C | 90 | Claude Code |
| whitebox-toolsznlgis/opengis-skills | Skill | 76 | 10d ago | MIT | A | 119 | Claude Code, Codex |
| pdalznlgis/opengis-skills | Skill | 76 | 10d ago | MIT | B | 90 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |